Multiply-and-Accumulate (MAC). The computational core of convolutional layers lies in the MAC operation. The amount of MAC per input image added up quickly from few thousands for LeNet network [1]} up to tens billions for VGG16 network [2]}. To meet this computation requirement, our accelerator embeds several MAC engines. Each of these works on an independent input filter, allowing the parallelization of the computation of the output activation map across multiple filters. Internally, each MAC is implemented with a set of multipliers and accumulators. Changing the number of those components directly affects the degree of parallelism.
